Model Architecture: Meta-Llama-3.1
- Input: Text
- Output: Text
Model Optimizations:
- Weight quantization: FP8
- Activation quantization: FP8
Intended Use Cases: Intended for commercial and research use in multiple languages. Similarly to Meta-Llama-3.1-405B-Instruct, this models is intended for assistant-like chat.
Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English.
Release Date: 8/22/2024
Version: 1.1
License(s): llama3.1
Model Developers: Neural Magic

This model is a quantized version of Meta-Llama-3.1-405B-Instruct. It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model, including multiple-choice, math reasoning, and open-ended text generation. Meta-Llama-3.1-405B-Instruct-FP8-dynamic achieves 99.0% recovery for the Arena-Hard evaluation, 100.0% for OpenLLM v1 (using Meta's prompting when available), 99.9% for OpenLLM v2, 100.2% for HumanEval pass@1, and 101.1% for HumanEval+ pass@1.

Model Optimizations

This model was obtained by quantizing the weights and activations of Meta-Llama-3.1-405B-Instruct to FP8 data type, ready for inference with vLLM built from source. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. In particular, this model can now be loaded and evaluated with a single node of 8xH100 GPUs, as opposed to multiple nodes.

Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-channel quantization is applied, in which a linear scaling per output dimension maps the FP8 representations of the quantized weights and activations. Activations are also quantized on a per-token dynamic basis. LLM Compressor is used for quantization.

Deployment

Use with vLLM

This model can be deployed efficiently using the vLLM backend, as shown in the example below.

python

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8-dynamic"
number_gpus = 8

sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256)

tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]

prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)

llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=4096)

outputs = llm.generate(prompts, sampling_params)

generated_text = outputs[0].outputs[0].text
print(generated_text)

vLLM aslo supports OpenAI-compatible serving. See the documentation for more details.

Creation

This model was created by applying LLM Compressor with calibration samples from UltraChat, as presented in the code snipet below.

python

import torch

from transformers import AutoTokenizer

from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
from llmcompressor.transformers.compression.helpers import (  # noqa
    calculate_offload_device_map,
    custom_offload_device_map,
)

recipe = """
quant_stage:
    quant_modifiers:
        QuantizationModifier:
            ignore: ["lm_head"]
            config_groups:
                group_0:
                    weights:
                        num_bits: 8
                        type: float
                        strategy: channel
                        dynamic: false
                        symmetric: true
                    input_activations:
                        num_bits: 8
                        type: float
                        strategy: token
                        dynamic: true
                        symmetric: true
                    targets: ["Linear"]
"""

model_stub = "meta-llama/Meta-Llama-3.1-405B-Instruct"
model_name = model_stub.split("/")[-1]

device_map = calculate_offload_device_map(
    model_stub, reserve_for_hessians=False, num_gpus=8, torch_dtype="auto"
)

model = SparseAutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map=device_map
)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
    output_dir=output_dir,
    save_compressed=True,
    tokenizer=AutoTokenizer.from_pretrained(model_stub),
)

Evaluation

This model was evaluated on the well-known Arena-Hard, OpenLLM v1, OpenLLM v2, HumanEval, and HumanEval+ benchmarks. In all cases, model outputs were generated with the vLLM engine.

Arena-Hard evaluations were conducted using the Arena-Hard-Auto repository. The model generated a single answer for each prompt form Arena-Hard, and each answer was judged twice by GPT-4. We report below the scores obtained in each judgement and the average.

OpenLLM v1 and v2 evaluations were conducted using Neural Magic's fork of lm-evaluation-harness (branch llama_3.1_instruct). This version of the lm-evaluation-harness includes versions of MMLU, ARC-Challenge and GSM-8K that match the prompting style of Meta-Llama-3.1-Instruct-evals and a few fixes to OpenLLM v2 tasks.

HumanEval and HumanEval+ evaluations were conducted using Neural Magic's fork of the EvalPlus repository.

Detailed model outputs are available as HuggingFace datasets for Arena-Hard, OpenLLM v2, and HumanEval.

Accuracy

Benchmark	Meta-Llama-3.1-405B-Instruct	Meta-Llama-3.1-405B-Instruct-FP8-dynamic (this model)	Recovery
Arena Hard	67.4 (67.3 / 67.5)	66.7 (66.7 / 66.6)	99.0%
OpenLLM v1
MMLU (5-shot)	87.4	87.5	100.0%
MMLU-cot (0-shot)	88.1	88.1	100.0%
ARC Challenge (0-shot)	95.0	95.0	100.0%
GSM-8K-cot (8-shot, strict-match)	96.0	95.8	99.8%
Hellaswag (10-shot)	88.5	88.5	99.9%
Winogrande (5-shot)	87.2	88.0	100.9%
TruthfulQA (0-shot, mc2)	65.3	65.3	99.9%
Average	86.8	86.9	100.0%
OpenLLM v2
MMLU-Pro (5-shot)	59.7	59.4	99.4%
IFEval (0-shot)	87.7	86.8	99.0%
BBH (3-shot)	67.0	67.1	100.1%
Math-\|v\|-5 (4-shot)	39.0	38.8	99.7%
GPQA (0-shot)	19.5	19.0	97.4%
MuSR (0-shot)	19.5	20.8	106.9%
Average	48.7	48.7	99.9%
Coding
HumanEval pass@1	86.8	87.0	100.2%
HumanEval+ pass@1	80.1	81.0	101.1%

Reproduction

The results were obtained using the following commands:

MMLU

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,max_gen_toks=10,tensor_parallel_size=8 \
  --tasks mmlu_llama_3.1_instruct \
  --apply_chat_template \
  --fewshot_as_multiturn \
  --num_fewshot 5 \
  --batch_size auto

MMLU-cot

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,max_gen_toks=1024,tensor_parallel_size=8 \
  --tasks mmlu_cot_0shot_llama_3.1_instruct \
  --apply_chat_template \
  --num_fewshot 0 \
  --batch_size auto

ARC-Challenge

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=8 \
  --tasks arc_challenge_llama_3.1_instruct \
  --apply_chat_template \
  --num_fewshot 0 \
  --batch_size auto

GSM-8K

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=8 \
  --tasks gsm8k_cot_llama_3.1_instruct \
  --apply_chat_template \
  --fewshot_as_multiturn \
  --num_fewshot 8 \
  --batch_size auto

Hellaswag

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=8 \
  --tasks hellaswag \
  --num_fewshot 10 \
  --batch_size auto

Winogrande

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=8 \
  --tasks winogrande \
  --num_fewshot 5 \
  --batch_size auto

TruthfulQA

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=8 \
  --tasks truthfulqa \
  --num_fewshot 0 \
  --batch_size auto

OpenLLM v2

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8-dynamic",dtype=auto,max_model_len=4096,tensor_parallel_size=8,enable_chunked_prefill=True \
  --apply_chat_template \
  --fewshot_as_multiturn \
  --tasks leaderboard \
  --batch_size auto

HumanEval and HumanEval+

Generation

python3 codegen/generate.py \
  --model neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8-dynamic \
  --bs 16 \
  --temperature 0.2 \
  --n_samples 50 \
  --root "." \
  --dataset humaneval \
  --tp 8

Sanitization

python3 evalplus/sanitize.py \
  humaneval/neuralmagic--Meta-Llama-3.1-405B-Instruct-FP8-dynamic_vllm_temp_0.2

Evaluation

evalplus.evaluate \
  --dataset humaneval \
  --samples humaneval/neuralmagic--Meta-Llama-3.1-405B-Instruct-FP8-dynamic_vllm_temp_0.2-sanitized

RedHatAI/Meta-Llama-3.1-405B-Instruct-FP8-dynamic

Meta-Llama-3.1-405B-Instruct-FP8-dynamic

Model Overview

Model Optimizations

Deployment

Use with vLLM

Creation

Evaluation

Accuracy

Reproduction

MMLU

MMLU-cot

ARC-Challenge

GSM-8K

Hellaswag

Winogrande

TruthfulQA

OpenLLM v2

HumanEval and HumanEval+

Generation

Sanitization

Evaluation

RedHatAI/
Meta-Llama-3.1-405B-Instruct-FP8-dynamic